Data Science History and Overview

What Is Data Science?

Data science is a discipline of applied mathematics and statistics that provides useful information based on large quantities of complex data or big data.

Data science, or data-driven science, integrates aspects of various disciplines with the assistance of computation to interpret volumes of data for decision-making purposes.

Key Takeaways

  • Data science utilizes techniques such as machine learning and artificial intelligence to extract meaningful information and to predict future patterns and behaviors.

  • Advances in technology, the internet, social media, and the use of technology have all increased access to big data.

  • The discipline of data science is developing as technology advances and large data collection and analysis techniques become more sophisticated.

  • Understanding Data Science

  • Data is drawn from diverse sectors, channels, and platforms, including cell phones, social media, e-commerce sites, healthcare surveys, and internet searches. The increase in the quantity of data available opened the door to a new field of study based on big data—the vast data sets that contribute to the creation of improved operational tools in all sectors.

The perpetually increasing access to data is possible due to advancements in technology and collection techniques. Individuals purchasing patterns and behavior can be monitored and predictions made based on the information obtained.

However, the ever-increasing data is unstructured and requires parsing for effective decision-making. This process is complex and time-consuming for companies—hence, the emergence of data science.

A Brief History of Data Science

Data Science Techniques in Algorithmic Trading: An In-Depth Analysis | by  Admarkon | Medium

The term "data science" has been in use since the early 1960s, when it was used synonymously with "computer science". Later, the term was made distinct to designate the survey of data processing methods used in a spectrum of diverse applications.

In 2001 William S. Cleveland used for the first time the term "data science" to refer to an independent discipline. The Harvard Business Review published an article in 2012 characterizing the function of the data scientist as the “sexiest job of the 21st century.”
Harvard Business Review. "Data Scientist: The Sexiest Job of the 21st Century." Accessed Sept. 12, 2021.

How Data Science Is Applied

Data science incorporates instruments from multiple disciplines to assemble a data set, process, and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. The disciplinary areas that make up the data science discipline include mining, statistics, machine learning, analytics, and programming.

Data mining applies algorithms to the complex data set to reveal patterns that are then used to extract useful and relevant data from the set. Statistical measures or predictive analytics use this extracted data to assess events that are likely to happen in the future based on what the data indicates happened in the past.

Machine learning is an artificial intelligence instrument that processes enormous quantities of data that a human would be unable to process in a lifetime. Machine learning perfects the decision model presented under predictive analytics by correlating the likelihood of an event happening to what actually happened at a predicted time.

Using analytics, the data analyst accumulates and processes the structured data from the machine learning stage using algorithms. The analyst interprets, converts, and summarizes the data into a cohesive language that the decision-making team can comprehend. Data science is applied to practically all contexts and, as the data scientist's function evolves, the discipline will expand to incorporate data architecture, data engineering, and data administration.

Data Scientists

Artificial intelligence, machine learning, data science: are these terms  interchangeable?

A data scientist accumulates, analyzes, and interprets large volumes of data, in many cases, to enhance a company's operations. Data scientist professionals develop statistical models that analyze data and detect patterns, trends, and relationships in data sets. This information can be used to predict consumer behavior or to identify business and operational risks.

The data scientist role is often that of a storyteller presenting data insights to decision-makers in a manner that is understandable and applicable to problem-solving.

Data Science Today

Companies are employing big data and data science to everyday activities to deliver value to consumers. Banking institutions are capitalizing on big data to enhance their fraud detection successes. Asset management firms are using big data to forecast the likelihood of a security’s price moving up or down at a stated time.

Companies such as Netflix exploit big data to determine what products to deliver to their consumers. Netflix also employs algorithms to generate personalized recommendations for users based on their viewing history. Data science is evolving at a rapid rate, and its applications will continue to alter lives into the future.

Don't All Sciences Use Data?

Yes, all empirical sciences acquire and analyze data. What separates data science is that it specializes in using sophisticated computational methods and machine learning techniques in order to process and analyze large data sets. Often, these data sets are so large or complex that they can't be adequately analyzed using traditional methods.

What Is Data Science Useful for?

Data science can identify patterns, facilitating the formulation of inferences and predictions, from apparently unstructured or unrelated data. Tech companies that collect user data can use techniques to transform what's collected into sources of useful or profitable information.

What Are Some Downsides of Data Science?

Data mining and efforts to commoditize personal data by social media companies have come under criticism in light of several controversies, such as Cambridge Analytica, where personal data was used by data scientists to influence political outcomes or undermine elections.

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